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This paper presents a novel approach to simulating micro-Doppler signatures caused by drones. The focus of this work is to produce realistic signatures that represent the variation that is observed in live radar measurements. In order to accomplish this, the kinematics and dynamics of a drone flight are modelled to capture the changing rotor rotation rates. The simulation results show realistic variation that is representative of measured drone flights.

This paper presents parametric investigation results on a staring FMCW radar system which targets drone swarms. The parametric investigation has been carried out by using the RAPID-SIM which facilitates system-level analysis of drone swarms' radar signatures. This paper explains concepts of the simulator's each module and also covers two parametric investigation results which deal with quantitative performance criteria for the design of the anti-drone swarms radar system.

The commercialization of drones has granted the public with unprecedented access to unmanned aviation. As such, the detection, tracking, and classification of drones in radars have become an area in high demand to mitigate accidental or voluntary misuse of these platforms. This paper focuses on the classification of drone targets in a safety context where the concept of Explainable AI is of particular interest. Here, we propose a simple, yet effective, means to extract a salient symmetry feature from the micro-Doppler signatures of drone targets, arising from onboard rotary components. Most importantly, this approach maintains the explainable nature of the employed recognition algorithm as the symmetry feature is directly related to the kinematics of the drones as the targets of interest. A large dataset collected from multiple locations with over 280 minutes of rotary and fixed wing drone flights has been collected and used to demonstrate the generalization capability of this approach.

This paper presents results of an experimental trial aimed at detecting a car and a flying drone, passively, by exploiting transmissions of opportunity from a staring Holographic radar. Results show that targets could be successfully detected using a low-cost passive receiver prototype. This may be considered a first step towards a radar network that could provide more comprehensive coverage, over a wider area, against a broader set of target types and without requiring additional spectral resources.